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COURSE DESCRIPTION AND APPLICATION INFORMATION

Course Name Code Semester T+A+L (hour/week) Type (C / O) Local Credit ECTS
Computational Intelligence Project EEE 405 Spring 01+00+04 Elective 3 8
Academic Unit: Electrical-Electronics Engineering
Mode of Delivery: Face to face
Prerequisites: EEE 206 Programming
Language of Instruction: English
Level of Course Unit: Undergraduate
Course Coordinator: - -
Course Objectives: To provide a foundation for concepts, models, algorithms, and tools for design and development of intelligent systems
Course Contents: Computational intelligence, machine learning, neural networks, genetic algorithms, fuzzy systems, project.(e.g. maze solving for robots, 5G traffic analysis, and anomaly detection)
Learning Outcomes of the Course Unit (LO):
  • 1- Ability to explain fundamental computational intelligence models.
  • 2- Ability to implement neural networks, genetic algorithms, and fuzzy systems.
  • 3- Ability to apply computational intelligence techniques to engineering problems such as optimization, control, classification, prediction, and pattern recognition.
  • 4- Ability to effectively engage in project works, write technical reports and present.
Planned Learning Activities and Teaching Methods: Lectures, individual and group projects, computing tools and coding


WEEKLY SUBJECTS AND RELATED PREPARATIONS

WeekSubjectsRelated Preperation
1 Computational intelligence: concepts and background Textbook: Ch. 1; selected papers
2 Neural networks: supervised and unsupervised learning Textbook: Ch. 2, 3, 4, 7
3 Computing tools, applications of neural networks, practical application report-1
4 Neural networks: radial basis function networks, SVM, LVQ Project: Topics and data sources Textbook: Ch. 5; selected papers
5 Evolutionary computation and genetic algorithms Textbook: Ch. 8-9-10
6 Project workshop
7 Fuzzy systems: fuzzy sets, fuzzy logic and inference, practical application report-2 Textbook: Ch. 20-21-23
8 Project: Data processing and feature selection
9 Project: Implementation-I
10 Project: Implementation-II, practical application report-3
11 Project: Implementation-III
12 Project: Implementation-IV
13 Project progress report
14 Review


REQUIRED AND RECOMMENDED READING

Computational Intelligence, Andries Engelbrecht, John Wiley, ISBN 978-0-470-03561-0, 2007.


OTHER COURSE RESOURCES

Advances in multi-objective nature inspired computing, Coello Coello, Carlos A., 2010. KHU: QA76.9.N37 A38 2010

Advances in computational intelligence: theory & applications, Wang, Fei-Yue, 2006. KHU: Q342 .A385 2006 EB


ASSESSMENT METHODS AND CRITERIA

Semester RequirementsNumberPercentage of Grade (%)
Attendance / Participation 14 15
In-Class Application Reports 3 30
Project Reports 1 15
Project Presentations (Faculty member review) 1 30
Project Presentations (Peer review) 1 10
Total: 20 100


WORKLOAD

EventsCountDuration (Hours)Total Workload (hour)
In-Class Studies where Faculty Members are Active14228
In-Class Studies where Students are Active14342
Out-of-Class Studies where Students are Active14570
Presentation of Project Reports16060
Total Workload (hour):200


THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)

# PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10
LO1                    
LO2                    
LO3                    
LO4